Search Optimization Algorithm Research Articles

Metaheuristic algorithms applied in ANN salinity modelling

Salinity is a classic problem in planning the quality of freshwater resources management. Recent studies related to hybrid machine learning models have shown it's capability to simulate salinity dynamics. However, previous studies of metaheuristic algorithms have not dealt with comparing single- and hybrid-based algorithms in much detail. The present study aimed to develop univariate salinity by applying an artificial neural network model (ANN) integrated with (hybrid-based) coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). The methodology was developed and tested using electrical conductivity (EC) and total dissolved solids (TDS) data collected from the Euphrates River in Babylon Province, Iraq, from 2010 to 2019. The CPSOCGSA performance was evaluated by various single-based ones, including multi-verse optimiser (MVO), marine predator's optimisation algorithm (MPA), particle swarm optimiser (PSO), and the slim mould algorithm (SMA). The principal finding here confirms that hybrid-based outperformed four single-based algorithms based on different criteria. The outcomes for TDS were 0.004, 0.0248, and 0.98 for CPSOCGSA-ANN technique concern scatter index (SI), root-mean-squared error (RMSE), and correlation coefficient (R2), respectively. For EC, the results were 0.96 for R2, 0.0386 for RMSE, and 0.006 for SI. Due to its predictive accuracy, the proposed CPSOCGSA-ANN approach is suggested as a potential strategy for predicting monthly salinity data. Considering agriculture's vital role in Babylon Province's economy, this study may help inform future freshwater quality management decisions.

A principal label space transformation and ridge regression-based hybrid gorilla troops optimization and jellyfish search algorithm for multi-label classification

A principal label space transformation and ridge regression-based hybrid gorilla troops optimization and jellyfish search algorithm for multi-label classification

Invasive weed optimization based compact hybridized fractal antenna design for RF energy harvesting and multifunctional wireless applications

This paper describes the optimal design and analysis of compact multiband Hybrid Fractal Antenna using Invasive Weed Optimization (HFAIWO). The configured structure involves the fusion of Minkowski, Sierpinski carpet and Giuseppe Peano in a coherent manner. The two-iterative conglomerated fractal antenna is realized physically by considering FR4 epoxy material (dielectric constant, εr = 4.4 and mass density = 1,900 kg/m3). The volumetric dimensions of the fabricated prototype are 36 x 36 x 1.6 mm3. In the designing process, the geometrical descriptors, namely, feedline width ‘WF’ and ground plane dimension ‘LG’ are optimized specifically using Invasive Weed Optimization (IWO) and Harmony Search Algorithm (HSA) strategies. The optimal solution is searched by changing the descriptor values under a set of practical constraints. The examined values of ‘WF’ and ‘LG’ using IWO and HSA are 3 and 31 mm, and 2.6 and 28.7 mm, respectively. Comparative results reveal that IWO offers superior solution quality and convergence than HSA. Afterwards, experimentation of the Projected HFAIWO is done to justify the recommended fractal hybridization approach. Measurements reveal that for VSWR ≤ 2, the fabricated structure produces nine resonance points (1.84, 3.99, 5.15, 5.55, 6.30, 6.58, 8.00, 9.29, and 12.01 GHz) with appreciable gain values. At the respective resonance points, the −10 dB impedance bandwidth is 3.9 %, 3.1 %, 2.1 %, 2.2 %, 1.4 %, 1.2 %, 6.9 %, 1.2 %, and 12.23 %. The provided radiation patterns are arbitrary bidirectional/omnidirectional. By applying the above-said approaches, the radiator size is reduced by 54 %. Importantly, the constructed structure covers two important bands i.e. 1.84 GHz (GSM 1800 band, downlink) and 5.55 GHz (WLAN (Lower) 5.150–5.725 GHz) associated with energy harvesting applications. The practical outcomes suggest that the introduced prototype is a proficient candidate for energy harvesting systems, Satellite communication for downlink, Long range tracking, Battlefield surveillance, Digital broadcast satellite service, Weather Radar, and Maritime navigation radar.

High-utility itemsets mining integrating an improved crow search algorithm and particle search optimization

High-utility itemsets mining integrating an improved crow search algorithm and particle search optimization

A novel squirrel-cat optimization based optimal expansion planning for distribution system

AimIn recent years, renewable distributed generation (DG) has grown to deliver sustainable electricity with minimal environmental impact. However, renewable DG poses new provocation in the distribution system expansion planning problem (DSEP). To address those problems, this paper suggests a new mathematical model for distribution system expansion plans using a novel hybrid optimization strategy. MethodThe optimal expansion plan for the distribution system is achieved using the hybrid optimization model, named Squirrel Search Insisted Cat Swarm Optimization (SSI-CS) algorithm. The proposed hybrid optimization algorithm is developed with the incorporation of the characteristics features of Squirrel search optimization (SSA) algorithm and Cat Swarm Optimization (CSO) Algorithm to optimize the solar capacity, wind capacity and biomass capacity. This combination aims to strike a balance between global and local optimization, ultimately leading to better cost-effective results. The Distribution systems variables like DG type, size/capacity, location, real power, reactive power, and the solar and wind capacity during load demand uncertainty act as the input to the proposed hybrid optimization algorithm. The main objective of attaining minimal cost for the expansion plan of the distribution system is checked, and the cycle is repeated until obtaining the optimal solution (minimum cost). ResultThe experimental analysis using an IEEE-33 bus system with 5 system states is executed in MATLAB/Simulink. The suggested SSI-CS model attained a minimal operational cost of 7433.4 which better than GWO, SSA and CSO. ConclusionHence, the proposed SSI-CS shows promise as an efficient and effective approach for distribution system expansion planning.

Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model

Determining the correct mushroom species with the necessary ecological characteristics is critical to continue mushroom production, which is essential in gastronomy. The mushroom farmers and collectors technique may help identify toxic mushrooms by detecting poisonous mushrooms using images of different mushroom species with distinctive morphological features. However, it can be not easy to distinguish between species. This paper used a dataset of 6714 mushroom images obtained from nine different mushroom species to classify the mushroom species. For a more straightforward comprehension of mushroom images and feature extraction by reanalysis of data sets, data visualization was performed using Grad-CAM, LIME, and Heatmap methods. Residual block-based Convolutional Neural Network (CNN) architectures are trained to automatically classify the concatenated feature map obtained from the Grad-CAM, LIME, and Heatmap methods. After extracting the deep features of the images from each architecture, the Atom Search Optimization (ASO) algorithm has been used to select the most distinctive features. The 6714×9000 size of the concatenated feature map was reduced to 6714×600 using the ASO algorithm. Classification results were evaluated using six different classifiers based on the feature map obtained to determine the mushroom species. The nine classes of mushroom species were classified successfully with 95.45 % accuracy using the proposed model with the ASO algorithm and KNN classifier. The methodology introduces novel visualization techniques for interpreting CNN-based models’ decisions in mushroom species classification tasks. Using metaheuristics-based CNN models with multi-feature fusion techniques allows the model to leverage diverse sources of information, potentially enhancing its ability to discriminate between mushroom species and achieve higher classification accuracy than existing methods. This study can advance the mushroom species classification field by introducing new methodologies, improving classification accuracy, providing insights into model interpretability, and facilitating knowledge transfer to related fields.

Stability prediction of roadway surrounding rock using INGO-RF

In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters, an INGO-RF roadway surrounding rock stability prediction model is proposed. This model combines the improved Northern Gok algorithm (INGO) and Random Forest (RF) based on the analysis of influencing factors of roadway surrounding rock stability. First, three strategies were employed to enhance the Northern Gob algorithm (NGO): Logistic chaotic mapping, refraction reverse learning, and improved sine and cosine. Subsequently, INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF. Secondly, a data set consisting of 34 groups of roadway surrounding rock data is selected. The input indexes of the model include roof strength, two-wall strength, floor strength, burial depth, roadway pillar width, ratio of direct roof thickness to mining height, and surrounding rock integrity. Meanwhile, surrounding rock stability is considered as the output index. Particle swarm optimization backpropagation neural network (PSO-BPNN), genetic algorithm optimization support vector machine (GA-SVM), Sparrow search algorithm optimization RF (SSA-RF) models were introduced to compare the prediction results with the INGO-RF model, and the results showed that: INGO-RF model has the best performance in the comparison of various performance indicators. Compared with other models, the accuracy rate (Ac) in the test set has increased by 0.12∼0.4, the accuracy rate (Pr) has increased by 0.07∼0.65, and the recall rate (Re) has increased by 0.08∼0.37. The harmonic mean (F1-Score) of the recall rate increased by 0.08∼0.52, the mean absolute error (MAE) decreased by 0.1428∼0.4285, the mean absolute percentage error (MAPE) decreased by 7.15%∼28.57%, and the root mean square error (RMSE) decreased by 0.1565∼0.3779. Finally, the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model. The results indicate that the predicted outcomes closely align with the actual results, demonstrating a certain level of reliability and stability. It shows that it can better meet the practical needs of engineering and avoid the occurrence of mine disasters.

A new integrated intelligent computing paradigm for predicting joints shear strength

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures. The prevailing models mostly adopt the form of empirical functions, employing mathematical regression techniques to represent experimental data. As an alternative approach, this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength. Five metaheuristic optimization algorithms, including the chameleon swarm algorithm (CSA), slime mold algorithm, transient search optimization algorithm, equilibrium optimizer and social network search algorithm, were employed to enhance the performance of the multilayered perception (MLP) model. Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models, employing statistical indicators such as root mean square error (RMSE), correlation coefficient (R2), mean absolute error (MAE), and variance accounted for (VAF) to evaluate the performance of each model. The sensitivity analysis of parameters that impact joints shear strength was conducted. Finally, the feasibility and limitations of this study were discussed. The results revealed that, in comparison to other models, the CSA-MLP model exhibited the most appropriate performance in terms of R2 (0.88), RMSE (0.19), MAE (0.15), and VAF (90.32%) values. The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength. This paper presented an efficacious attempt toward swift prediction of joints shear strength, thus avoiding the need for costly in-site and laboratory tests.

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary electricity sources in Saudi Arabia. This investigation aims to reduce the overall costs and emissions of electricity production from thermal units operated by the Shuqaiq Water and Electricity Company. It also considers practical thermoelectric constraints, such as power losses, ramp-rate constraints, and environmental optimization functions, to align with the sustainability goals of Saudi Vision 2030. Optimizing electric energy delivery is crucial within an energy control center. The focus of this work is to develop an advanced computational technique that reduces reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination was conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on the tie-line power flow constraints. Additionally, the study also involved RETScreen modelling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analyses offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduced the electricity cost by 2.29% and 3.70% in hybrid energy networks while effectively handling the system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreased toxic emissions from power plants while also supporting the sustainability objectives of United Nations SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization

Introduction: The principle of passive sound control is based on the phenomenon of sound absorption by absorbers. The factors affecting sound absorption include porosity, pore size, pore opening size, thickness, and air flow resistance. Materials and methods: In this study, the authors compared the optimization results of the effective parameters on sound absorption coefficient (AC) using the three optimization methods: Guided Local Search (GLS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The programming was done in MATLAB software. Thicknesses of 5, 10, 20, 30 and 40 mm were chosen for optimization at frequencies of 500 to 3000 Hz. Results: In frequencies above 2 kHz (thickness 5 to 40 mm), the three optimal methods had the same performance and estimated AC of 1. At low frequencies of 2 kHz and thicknesses of 30 and 40 mm, GA and PSO methods obtained an AC of 1. Conclusion: It seems that the GA and PSO optimization algorithm are suitable methods to optimize the AC of metal foam in low and high frequencies.

Enhancing natural disaster analysis and waste classification: a novel VGG-FL approach.

The study of natural disasters is a crucial field that involves analyzing the occurrence, impact, and aftermath of various natural hazards that can cause significant harm to communities and the environment. Efficient waste management and environmental protection require proper classification of waste. Analyzing natural disasters and categorizing waste can be a time-consuming task, and conventional methods often struggle with it. However, a new approach called Visual Geometry Group with Federated Learning (VGG-FL) has been introduced to address these challenges. This methodology uses the golden search optimization (GSO) algorithm for feature selection and leverages VGG with federated learning for feature extraction and classification. To test the effectiveness of this method, a disaster image dataset was used to train the VGG-FL model. The results showed that the VGG-FL model attained exceptional accuracy in discerning and categorizing various disaster scenarios. The waste classification dataset simultaneously trains the VGG-FL model to categorize waste based on its characteristics and potential hazards. To measure the model's performance, several evaluation metrics such as accuracy, specificity, precision, F1-score, and recall are utilized to assess the effectiveness of the proposed VGG-FL method. These results are then compared with existing methodologies. The VGG-FL method performs exceptionally well, achieving 98.52% accuracy, 97.48% precision, 97.83% recall, 97.58% F1-score, and 97.12% specificity. These experimental findings demonstrate the efficacy of the VGG-FL method in analyzing natural disasters and classifying waste materials.

Hybrid random forest models optimized by Sparrow search algorithm (SSA) and Harris hawk optimization algorithm (HHO) for slope stability prediction

The intelligent prediction of slope stability is crucial for slope management and aboveground structure design. Due to the complexity and nonlinearity of slope stability evaluation, intelligent prediction models often perform better. This study developed two optimizers, the Sparrow search algorithm (SSA) and the Harris hawk optimization algorithm (HHO), to optimize the random forest (RF) model for evaluating slope stability. A database of 444 slope cases was established. The data was split into 80% for training and 20% for testing. The optimizers were used to determine the hyperparameters of the models. Four classical classification models were introduced for comparison. Five-fold cross-validation was employed, and model performance was evaluated using accuracy, precision, recall, and F1-score. A comprehensive method was developed for evaluating the performance of models. The SSA-RF model exhibited the best performance with the highest accuracy (0.9101), precision (0.9500), recall (0.8636), F1-score (0.9048), and AUC (0.9407). The Gini coefficient indicated that unit weight was the most influential indicator (41.16). These methods offer a promising approach for predicting slope stability, applicable in slope engineering practice.

Multiobjective flexible job-shop scheduling optimization for manufacturing servitization

PurposeWith the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.Design/methodology/approachAn improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.FindingsThrough the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.Practical implicationsThe study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.Originality/valueThe study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.

Comparative Analysis of Gravitational Search Algorithm and Particle Swarm Optimization for Solar MPPT

This work delves into the optimization of Maximum Power Point Tracking (MPPT) for photovoltaic (PV) systems through a comparative analysis of two advanced algorithms: the Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). With the escalating demand for renewable energy sources, enhancing the efficiency of solar panels has become crucial. MPPT techniques are pivotal in maximizing the power output from solar panels by adjusting to varying environmental conditions. This work implements GSA and PSO within a MATLAB environment to track the MPP of a solar cell array efficiently. The study systematically evaluates the performance, convergence speed, and stability of both algorithms under diverse operational scenarios. Preliminary findings indicate that GSA exhibits faster convergence and reduced oscillatory behaviour compared to PSO, suggesting a superior efficiency in tracking the MPP. This paper aims to provide a comprehensive comparison between GSA and PSO, offering insights into their applicability and effectiveness in optimizing solar energy systems, thereby contributing to the development of more efficient renewable energy solutions.

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

ReLU-oscillator: Chaotic VGG10 model for real-time neural style transfer on painting authentication

Neural Style Transfer (NST) has exerted algorithms to generate animation images in computer vision for decades. The Convolution Neural Network (CNN) applied to image content and styles in the NST has improved the extraction of functionalities and the calculation of the convergence speed to recognize and generate high quality structure images, but unpredictable loss elements are inadequate to iterate human learning ability of unique artists’ paintings or styles. This paper offers a chaotic VGG10 NST model based on CNN, ReLU and Lee-Oscillator. The proposed ReLU-Oscillator dynamically relies on activation functions in a chaotic state that dynamically improves high-frequency iterative training of high-quality image and high-speed time optimization. In addition, the best parameters recognizing and determining a personalized painting style from the search for ReLU-Oscillator by corresponding to a set of parameters from proposed Optimal Oscillator Parameter Search Algorithm. Experimental results showed that the stylized image generated by the Chaotic VGG 10 model with high-frequency oscillation succeeded in reducing the training time in magnitude models with the smallest Params and FLOPs in model performance and image quality with the lowest content loss for preserving semantic information and moderate style loss for style similarity balanced with comparison to 8 state-of-the-art models in visual perception evaluation. Chaotic NST has a unique identification for each artist supplemented with a set of oscillator parameters to evaluate the loss performance based on the relative error between the famous painting and its imitation, indicating that the authentication of paintings can be detected through specific ReLU-Oscillator parameters for each stylization, estimated from the loss value performance.

Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production

In agriculture, irrigation is essential for providing water to crops based on the type of soil they are grown in. To achieve success in farming, it is important to evaluate soil fertility, temperature, rainfall and set irrigation schedules. In this work, to enhance agricultural production, an IoT-based hydration system that utilizes soil moisture and humidity sensors to keep an eye on soil conditions and water crops precisely is developed. This system effectively manages water usage in farming, resulting in an efficient conservation of water resources. The proposed model is categorized into four main phases: (a) Pre-processing; (b) Clustering; (c) Feature extraction; (d) Classification. Initially, the collected raw data is pre-processed via a data-cleaning approach. From the pre-processed data, DB scan is used to cluster the data. From the clustered data, the important feature is extracted by using statistical features like mean, standard deviation, kurtosis, and skewness. Subsequently, from the extracted features, the most optimal features are selected via a new hybrid meta-heuristic optimization model referred to as Cuckoo search-based Levy adolescent identity search algorithm (CLAIS). The projected CLAIS model is the conceptual amalgamation of the standard Cuckoo search optimization (CSO) and adolescent identity search algorithm (AISA), respectively. CLAIS-based deep Q network (CLAIS-DQN) classifier is used to classify the optimal features. DQN is an efficient solution to offload the request optimally which improves the overall performance of the network. The proposed model is implemented using the PYTHON platform. The proposed model has recorded the highest detection accuracy as 96%.

Research on stock trend prediction based on grey wolf optimized RF and SVM models

The volatility of the stock market has a profound impact on the stability of the financial market, and then has a significant effect on the overall economic situation, so the accurate prediction of stock trend has always been the focus of research in the financial field. In this paper, the raw minute-level stock data of SSE 50 and CSI 300 in 2021 are normalized, and the historical training and test sets of daily stocks for model training and testing are established based on the datasets. The RF-SVM model based on the gray wolf optimization algorithm is constructed to compare the traditional support vector machine and random forest model, and the relevant performance indexes are calculated. In this study, the RF-SVM model based on gray wolf optimization is highly correlated with the characteristics of stock data, and the prediction accuracy of stock index data is more than 85%. The cross-validation score is over 0.77 and the overall weighted score is as high as 0.98. Compared with the SVM and random forest models under the default parameters, the RF-SVM model based on gray wolf optimization has an average advantage of 25% in performance indicators. The generalization ability is increased by about 26%; The fitting error was reduced by about 51.15%; The overall average score advantage is about 34%. These results prove that the RF-SVM model of gray wolf optimization has superior performance and significant optimization effect in stock prediction, and compared with the traditional grid search optimization algorithm, gray wolf optimization has excellent adaptability and can gradually obtain the global optimal parameter configuration.

Global MPPT controllers for enhancing dynamic performance of photovoltaic systems under partial shading condition

This research focuses on the development and evaluation of global maximum power point tracking MPPT controllers for enhancing the dynamic performance of Photovoltaic (PV) systems under partial shading conditions. Three different controllers, namely Proportional-Integral (PI), Fractional-Order PI (FOPI), and Fuzzy Logic Controllers (FLC), are studied in conjunction with a modified Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) algorithms. The proposed controllers with a novel optimization algorithm called Atom Search Optimization Algorithm (ASOA) are compared with classical control. The objective is to investigate the effectiveness of the global MPPT controllers in achieving higher tracking accuracy, faster convergence, and improved stability under varying shading conditions. The study involves extensive simulations using a representative PV system model subjected to different shading scenarios. The results demonstrate that the proposed global MPPT controllers with the ASOA algorithm, when integrated with the modified MPPT algorithms, outperform the classical control techniques in terms of dynamic performance and response to partial shading conditions. The suggested ASOA-based fuzzy IC MPPT controllers can reach the MPP more quickly with a shorter settling time and a better tracking response. Furthermore, with a reduced settling time of about 1.9 % and an average power harvesting efficiency of over 97.9 %, ASOA-based fuzzy IC MPPT controllers offer the best harvesting characteristics. The controller exhibits superior tracking accuracy and stability, mitigating the effects of shading-induced multiple peaks in the P-V curve.

Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.